Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Croissant: A metadata format for ml-ready datasets
Data is a critical resource for machine learning (ML), yet working with data remains a key
friction point. This paper introduces Croissant, a metadata format for datasets that creates a …
friction point. This paper introduces Croissant, a metadata format for datasets that creates a …
Orion: Interference-aware, fine-grained GPU sharing for ML applications
GPUs are critical for maximizing the throughput-per-Watt of deep neural network (DNN)
applications. However, DNN applications often underutilize GPUs, even when using large …
applications. However, DNN applications often underutilize GPUs, even when using large …
Fastflow: Accelerating deep learning model training with smart offloading of input data pipeline
When training a deep learning (DL) model, input data are pre-processed on CPUs and
transformed into tensors, which are then fed into GPUs for gradient computations of model …
transformed into tensors, which are then fed into GPUs for gradient computations of model …
An overview of the data-loader landscape: Comparative performance analysis
The efficiency of Deep Learning (DL) training jobs is critically dependent on dataloaders,
which facilitate the transfer of data from storage to DL-accelerated hardware during training …
which facilitate the transfer of data from storage to DL-accelerated hardware during training …
Pecan:{Cost-Efficient}{ML} Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement
Input data preprocessing is a common bottleneck in machine learning (ML) jobs, that can
significantly increase training time and cost as expensive GPUs or TPUs idle waiting for …
significantly increase training time and cost as expensive GPUs or TPUs idle waiting for …
Where is my training bottleneck? hidden trade-offs in deep learning preprocessing pipelines
Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep
the training processes busy. Maximizing resource utilization is becoming more challenging …
the training processes busy. Maximizing resource utilization is becoming more challenging …
[HTML][HTML] Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers
Data pipelines are an integral part of various modern data-driven systems. However, despite
their importance, they are often unreliable and deliver poor-quality data. A critical step …
their importance, they are often unreliable and deliver poor-quality data. A critical step …
tf. data service: A case for disaggregating ML input data processing
Machine learning (ML) computations commonly execute on expensive specialized
hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt …
hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt …
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models
Training recommendation systems (RecSys) faces several challenges as it requires the
“data preprocessing” stage to preprocess an ample amount of raw data and feed them to the …
“data preprocessing” stage to preprocess an ample amount of raw data and feed them to the …
Intune: Reinforcement learning-based data pipeline optimization for deep recommendation models
Deep learning-based recommender models (DLRMs) have become an essential component
of many modern recommender systems. Several companies are now building large compute …
of many modern recommender systems. Several companies are now building large compute …